Towards Improved Medical Image Segmentation Using Deep Learning

Tumor Segmentation ◽

Specific Information ◽

Multiple Imaging ◽

Benchmark Datasets ◽

Representational Power

Convolutional neural networks have been asserted to be fast and precise frameworks with great potential in image segmentation. Within the medical domain, image segmentation is a pre-cursor to several applications including surgical simulations, treatment planning and patient prognosis. In this thesis, we attempt to solve two major limitations of current segmentation practices: 1) dealing with unbalanced classes and 2) dealing with multiple modalities. In medical imaging, unbalanced classes present as the regions of interest that are typically significantly smaller in volume than the background class or other classes. We propose an improvement to the current gold standard cost function to boost the focus of the network to the smaller classes. Another problem within medical imaging is the variation in both anatomy and pathology across patients. Utilizing multiple imaging modalities provides complementary, segmentation-specific information and is commonly employed by radiologists when contouring data. We propose a image fusion strategy for multi-modal data that uses the variation in modality specific features to guide the task specific learning. Together, our contributions propose a framework to maximize the representational power of the dataset using models with less complexity and higher generalizability. Our contributions outperform baseline models for multi-class segmentation and are modular enough to be scaled up to deeper networks. We demonstrate the effectiveness of the proposed cost function and multimodal framework, both individually and together, on benchmark datasets including the Breast Ultrasound Dataset B (BUS) [1], the International Skin Imaging Collaboration (ISIC 2018) [2], [3] and the Brain Tumor Segmentation Challenge (BraTs 2018) [4]. In all experiments, the proposed methods match or outperform the baseline methods while employing simpler networks

Towards Improved Medical Image Segmentation Using Deep Learning

10.32920/ryerson.14663766.v1 ◽

2021 ◽

Author(s):

Nabila Abraham

Keyword(s):

Image Segmentation ◽

Medical Imaging ◽

Cost Function ◽

Tumor Segmentation ◽

Specific Information ◽

Multiple Imaging ◽

Benchmark Datasets ◽

Representational Power

A Novel Brain Image Segmentation Method Using an Improved 3D U-Net Model

Scientific Programming ◽

10.1155/2021/4801077 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Zhuqing Yang

Keyword(s):

Image Segmentation ◽

Medical Image ◽

Three Dimensional ◽

Research Field ◽

Tumor Segmentation ◽

Segmentation Method ◽

Segmentation Methods ◽

Three Dimensional Segmentation

Medical image segmentation (IS) is a research field in image processing. Deep learning methods are used to automatically segment organs, tissues, or tumor regions in medical images, which can assist doctors in diagnosing diseases. Since most IS models based on convolutional neural network (CNN) are two-dimensional models, they are not suitable for three-dimensional medical imaging. On the contrary, the three-dimensional segmentation model has problems such as complex network structure and large amount of calculation. Therefore, this study introduces the self-excited compressed dilated convolution (SECDC) module on the basis of the 3D U-Net network and proposes an improved 3D U-Net network model. In the SECDC module, the calculation amount of the model can be reduced by 1 × 1 × 1 convolution. Combining normal convolution and cavity convolution with an expansion rate of 2 can dig out the multiview features of the image. At the same time, the 3D squeeze-and-excitation (3D-SE) module can realize automatic learning of the importance of each layer. The experimental results on the BraTS2019 dataset show that the Dice coefficient and other indicators obtained by the model used in this paper indicate that the overall tumor can reach 0.87, the tumor core can reach 0.84, and the most difficult to segment enhanced tumor can reach 0.80. From the evaluation indicators, it can be analyzed that the improved 3D U-Net model used can greatly reduce the amount of data while achieving better segmentation results, and the model has better robustness. This model can meet the clinical needs of brain tumor segmentation methods.

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence ◽

Medical Image Segmentation using Squeeze-and-Expansion Transformers

10.24963/ijcai.2021/112 ◽

2021 ◽

Author(s):

Shaohua Li ◽

Xiuchao Sui ◽

Xiangde Luo ◽

Xinxing Xu ◽

Yong Liu ◽

...

Keyword(s):

Image Segmentation ◽

Medical Image ◽

Receptive Fields ◽

Image Features ◽

Tumor Segmentation ◽

Large Context ◽

Mri Scans ◽

Big Picture

Medical image segmentation is important for computer-aided diagnosis. Good segmentation demands the model to see the big picture and fine details simultaneously, i.e., to learn image features that incorporate large context while keep high spatial resolutions. To approach this goal, the most widely used methods -- U-Net and variants, extract and fuse multi-scale features. However, the fused features still have small "effective receptive fields" with a focus on local image cues, limiting their performance. In this work, we propose Segtran, an alternative segmentation framework based on transformers, which have unlimited "effective receptive fields" even at high feature resolutions. The core of Segtran is a novel Squeeze-and-Expansion transformer: a squeezed attention block regularizes the self attention of transformers, and an expansion block learns diversified representations. Additionally, we propose a new positional encoding scheme for transformers, imposing a continuity inductive bias for images. Experiments were performed on 2D and 3D medical image segmentation tasks: optic disc/cup segmentation in fundus images (REFUGE'20 challenge), polyp segmentation in colonoscopy images, and brain tumor segmentation in MRI scans (BraTS'19 challenge). Compared with representative existing methods, Segtran consistently achieved the highest segmentation accuracy, and exhibited good cross-domain generalization capabilities.

A Deep Learning Framework for Segmenting Brain Tumors Using MRI and Synthetically Generated CT Images

Sensors ◽

10.3390/s22020523 ◽

2022 ◽

Vol 22 (2) ◽

pp. 523

Author(s):

Kh Tohidul Islam ◽

Sudanthi Wijewickrema ◽

Stephen O’Leary

Keyword(s):

Image Segmentation ◽

Three Dimensional ◽

Disease Diagnosis ◽

Ct Images ◽

Tumor Segmentation ◽

Learning Framework ◽

Guided Surgery ◽

Magnetic Resonance Imaging Mri

Multi-modal three-dimensional (3-D) image segmentation is used in many medical applications, such as disease diagnosis, treatment planning, and image-guided surgery. Although multi-modal images provide information that no single image modality alone can provide, integrating such information to be used in segmentation is a challenging task. Numerous methods have been introduced to solve the problem of multi-modal medical image segmentation in recent years. In this paper, we propose a solution for the task of brain tumor segmentation. To this end, we first introduce a method of enhancing an existing magnetic resonance imaging (MRI) dataset by generating synthetic computed tomography (CT) images. Then, we discuss a process of systematic optimization of a convolutional neural network (CNN) architecture that uses this enhanced dataset, in order to customize it for our task. Using publicly available datasets, we show that the proposed method outperforms similar existing methods.

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence ◽

Unsupervised Domain Adaptation with Dual-Scheme Fusion Network for Medical Image Segmentation

10.24963/ijcai.2020/455 ◽

2020 ◽

Author(s):

Danbing Zou ◽

Qikui Zhu ◽

Pingkun Yan

Keyword(s):

Image Segmentation ◽

Medical Image ◽

Network Performance ◽

Domain Adaptation ◽

Training Data ◽

Tumor Segmentation ◽

Target Domain ◽

Unsupervised Domain Adaptation

Domain adaptation aims to alleviate the problem of retraining a pre-trained model when applying it to a different domain, which requires large amount of additional training data of the target domain. Such an objective is usually achieved by establishing connections between the source domain labels and target domain data. However, this imbalanced source-to-target one way pass may not eliminate the domain gap, which limits the performance of the pre-trained model. In this paper, we propose an innovative Dual-Scheme Fusion Network (DSFN) for unsupervised domain adaptation. By building both source-to-target and target-to-source connections, this balanced joint information flow helps reduce the domain gap to further improve the network performance. The mechanism is further applied to the inference stage, where both the original input target image and the generated source images are segmented with the proposed joint network. The results are fused to obtain more robust segmentation. Extensive experiments of unsupervised cross-modality medical image segmentation are conducted on two tasks -- brain tumor segmentation and cardiac structures segmentation. The experimental results show that our method achieved significant performance improvement over other state-of-the-art domain adaptation methods.

End-to-End Boundary Aware Networks for Medical Image Segmentation

10.1101/770248 ◽

2019 ◽

Author(s):

Ali Hatamizadeh ◽

Demetri Terzopoulos ◽

Andriy Myronenko

Keyword(s):

Image Segmentation ◽

Medical Image ◽

Medical Image Analysis ◽

Tumor Segmentation ◽

Anatomical Structures ◽

Fully Convolutional Neural Networks ◽

Boundary Information ◽

End To End

AbstractFully convolutional neural networks (CNNs) have proven to be effective at representing and classifying textural information, thus transforming image intensity into output class masks that achieve semantic image segmentation. In medical image analysis, however, expert manual segmentation often relies on the boundaries of anatomical structures of interest. We propose boundary aware CNNs for medical image segmentation. Our networks are designed to account for organ boundary information, both by providing a special network edge branch and edge-aware loss terms, and they are trainable end-to-end. We validate their effectiveness on the task of brain tumor segmentation using the BraTS 2018 dataset. Our experiments reveal that our approach yields more accurate segmentation results, which makes it promising for more extensive application to medical image segmentation.

The Brain Tumor Segmentation Using Fuzzy C-Means Technique

Computer Vision ◽

10.4018/978-1-5225-5204-8.ch106 ◽

2018 ◽

pp. 2402-2419

Author(s):

Jyotsna Rani ◽

Ram Kumar ◽

Fazal A. Talukdar ◽

Nilanjan Dey

Keyword(s):

Image Segmentation ◽

Brain Tumor ◽

Tumor Segmentation ◽

Fuzzy C Means ◽

Mri Brain ◽

Area Of Interest ◽

Fuzzy C Means Clustering ◽

Comparative Results ◽

The Brain

Image segmentation is a technique which divides an image into its constituent regions or objects. Segmentation continues till we reach our area of interest or the specified object of target. This field offers vast future scope and challenges for the researchers. This proposal uses the fuzzy c mean technique to segment the different MRI brain tumor images. This proposal also shows the comparative results of Thresholding, K-means clustering and Fuzzy c- means clustering. Dice coefficient and Jaccards measure is used for accuracy of the segmentation in this proposal. Experimental results demonstrate the performance of the designed method.

Multi-Task Deep Supervision on Attention R2U-Net for Brain Tumor Segmentation

Frontiers in Oncology ◽

10.3389/fonc.2021.704850 ◽

2021 ◽

Vol 11 ◽

Author(s):

Shiqiang Ma ◽

Jijun Tang ◽

Fei Guo

Keyword(s):

Deep Learning ◽

Brain Tumor ◽

Computational Cost ◽

Validation Dataset ◽

Tumor Segmentation ◽

Proposed Model ◽

Fusion Methods ◽

Tumor Area

Accurate automatic medical image segmentation technology plays an important role for the diagnosis and treatment of brain tumor. However, simple deep learning models are difficult to locate the tumor area and obtain accurate segmentation boundaries. In order to solve the problems above, we propose a 2D end-to-end model of attention R2U-Net with multi-task deep supervision (MTDS). MTDS can extract rich semantic information from images, obtain accurate segmentation boundaries, and prevent overfitting problems in deep learning. Furthermore, we propose the attention pre-activation residual module (APR), which is an attention mechanism based on multi-scale fusion methods. APR is suitable for a deep learning model to help the network locate the tumor area accurately. Finally, we evaluate our proposed model on the public BraTS 2020 validation dataset which consists of 125 cases, and got a competitive brain tumor segmentation result. Compared with the state-of-the-art brain tumor segmentation methods, our method has the characteristics of a small parameter and low computational cost.

Deep Learning Applications in Medical Imaging - Advances in Medical Technologies and Clinical Practice ◽

Deep Learning for Medical Image Segmentation

10.4018/978-1-7998-5071-7.ch002 ◽

2021 ◽

pp. 40-77

Author(s):

Kanchan Sarkar ◽

Bohang Li

Keyword(s):

Image Segmentation ◽

Deep Learning ◽

Medical Imaging ◽

Medical Image ◽

Disease Diagnosis ◽

Kidney Tumour ◽

Tumour Segmentation ◽

Recent Innovation ◽

Segmentation Algorithms

Pixel accurate 2-D, 3-D medical image segmentation to identify abnormalities for further analysis is on high demand for computer-aided medical imaging applications. Various segmentation algorithms have been studied and applied in medical imaging for many years, but the problem remains challenging due to growing a large number of variety of applications starting from lung disease diagnosis based on x-ray images, nucleus detection, and segmentation based on microscopic pictures to kidney tumour segmentation. The recent innovation in deep learning brought revolutionary advances in computer vision. Image segmentation is one such area where deep learning shows its capacity and improves the performance by a larger margin than its successor. This chapter overviews the most popular deep learning-based image segmentation techniques and discusses their capabilities and basic advantages and limitations in the domain of medical imaging.

MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning

BMC Medical Imaging ◽

10.1186/s12880-020-00543-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Dominik Müller ◽

Frank Kramer

Keyword(s):

Image Segmentation ◽

Deep Learning ◽

Medical Image ◽

Cross Validation ◽

Data Augmentation ◽

Semantic Segmentation ◽

Tumor Segmentation ◽

Data Set ◽

Public Data

Abstract Background The increased availability and usage of modern medical imaging induced a strong need for automatic medical image segmentation. Still, current image segmentation platforms do not provide the required functionalities for plain setup of medical image segmentation pipelines. Already implemented pipelines are commonly standalone software, optimized on a specific public data set. Therefore, this paper introduces the open-source Python library MIScnn. Implementation The aim of MIScnn is to provide an intuitive API allowing fast building of medical image segmentation pipelines including data I/O, preprocessing, data augmentation, patch-wise analysis, metrics, a library with state-of-the-art deep learning models and model utilization like training, prediction, as well as fully automatic evaluation (e.g. cross-validation). Similarly, high configurability and multiple open interfaces allow full pipeline customization. Results Running a cross-validation with MIScnn on the Kidney Tumor Segmentation Challenge 2019 data set (multi-class semantic segmentation with 300 CT scans) resulted into a powerful predictor based on the standard 3D U-Net model. Conclusions With this experiment, we could show that the MIScnn framework enables researchers to rapidly set up a complete medical image segmentation pipeline by using just a few lines of code. The source code for MIScnn is available in the Git repository: https://github.com/frankkramer-lab/MIScnn.